Overview

Dataset statistics

Number of variables18
Number of observations8986
Missing cells387
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 MiB
Average record size in memory1.3 KiB

Variable types

Numeric7
Text6
Categorical2
DateTime1
Boolean1
Path1

Alerts

status has constant value "Released"Constant
adult has constant value "False"Constant
budget is highly overall correlated with revenue and 1 other fieldsHigh correlation
popularity is highly overall correlated with revenue and 1 other fieldsHigh correlation
revenue is highly overall correlated with budget and 2 other fieldsHigh correlation
vote_count is highly overall correlated with budget and 2 other fieldsHigh correlation
original_language is highly imbalanced (74.8%)Imbalance
keywords has 328 (3.7%) missing valuesMissing
popularity is highly skewed (γ1 = 28.71123438)Skewed
id has unique valuesUnique
revenue has 2458 (27.4%) zerosZeros
budget has 2855 (31.8%) zerosZeros

Reproduction

Analysis started2024-05-13 11:57:16.869950
Analysis finished2024-05-13 11:57:41.508649
Duration24.64 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct8986
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176470.94
Minimum5
Maximum1140066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:57:41.823832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile688.5
Q19905.25
median33607.5
Q3333672.75
95-th percentile646388
Maximum1140066
Range1140061
Interquartile range (IQR)323767.5

Descriptive statistics

Standard deviation233173.92
Coefficient of variation (CV)1.3213162
Kurtosis0.63892994
Mean176470.94
Median Absolute Deviation (MAD)32613
Skewness1.2755955
Sum1.5857679 × 109
Variance5.4370076 × 1010
MonotonicityNot monotonic
2024-05-13T11:57:42.382602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27205 1
 
< 0.1%
508791 1
 
< 0.1%
123109 1
 
< 0.1%
12246 1
 
< 0.1%
28597 1
 
< 0.1%
10516 1
 
< 0.1%
8974 1
 
< 0.1%
51170 1
 
< 0.1%
318256 1
 
< 0.1%
11860 1
 
< 0.1%
Other values (8976) 8976
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
1140066 1
< 0.1%
1102776 1
< 0.1%
1083862 1
< 0.1%
1077280 1
< 0.1%
1070514 1
< 0.1%
1067282 1
< 0.1%
1040330 1
< 0.1%
1040148 1
< 0.1%
1024530 1
< 0.1%
1016121 1
< 0.1%

title
Text

Distinct8686
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size712.2 KiB
2024-05-13T11:57:43.436188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length83
Median length59
Mean length15.704207
Min length1

Characters and Unicode

Total characters141118
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8413 ?
Unique (%)93.6%

Sample

1st rowInception
2nd rowInterstellar
3rd rowThe Dark Knight
4th rowAvatar
5th rowThe Avengers
ValueCountFrequency (%)
the 2946
 
11.5%
of 809
 
3.2%
a 384
 
1.5%
and 278
 
1.1%
in 269
 
1.1%
2 207
 
0.8%
to 190
 
0.7%
154
 
0.6%
man 143
 
0.6%
i 108
 
0.4%
Other values (7138) 20054
78.5%
2024-05-13T11:57:45.116623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16556
 
11.7%
e 14492
 
10.3%
a 8844
 
6.3%
o 8322
 
5.9%
n 7591
 
5.4%
r 7492
 
5.3%
i 7324
 
5.2%
t 7025
 
5.0%
h 5478
 
3.9%
s 5437
 
3.9%
Other values (105) 52557
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16556
 
11.7%
e 14492
 
10.3%
a 8844
 
6.3%
o 8322
 
5.9%
n 7591
 
5.4%
r 7492
 
5.3%
i 7324
 
5.2%
t 7025
 
5.0%
h 5478
 
3.9%
s 5437
 
3.9%
Other values (105) 52557
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16556
 
11.7%
e 14492
 
10.3%
a 8844
 
6.3%
o 8322
 
5.9%
n 7591
 
5.4%
r 7492
 
5.3%
i 7324
 
5.2%
t 7025
 
5.0%
h 5478
 
3.9%
s 5437
 
3.9%
Other values (105) 52557
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16556
 
11.7%
e 14492
 
10.3%
a 8844
 
6.3%
o 8322
 
5.9%
n 7591
 
5.4%
r 7492
 
5.3%
i 7324
 
5.2%
t 7025
 
5.0%
h 5478
 
3.9%
s 5437
 
3.9%
Other values (105) 52557
37.2%

vote_average
Real number (ℝ)

Distinct2989
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.64491
Minimum2.098
Maximum9.172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:57:45.727664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.098
5-th percentile5.31125
Q16.11525
median6.6675
Q37.222
95-th percentile7.9
Maximum9.172
Range7.074
Interquartile range (IQR)1.10675

Descriptive statistics

Standard deviation0.79953579
Coefficient of variation (CV)0.12032304
Kurtosis0.26663939
Mean6.64491
Median Absolute Deviation (MAD)0.5535
Skewness-0.33450856
Sum59711.161
Variance0.63925747
MonotonicityNot monotonic
2024-05-13T11:57:46.375507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 49
 
0.5%
6.2 47
 
0.5%
6.9 46
 
0.5%
6.4 45
 
0.5%
7.1 44
 
0.5%
6.6 42
 
0.5%
7 42
 
0.5%
6.7 42
 
0.5%
7.5 39
 
0.4%
6.3 38
 
0.4%
Other values (2979) 8552
95.2%
ValueCountFrequency (%)
2.098 1
< 0.1%
2.89 1
< 0.1%
3.152 1
< 0.1%
3.228 1
< 0.1%
3.23 1
< 0.1%
3.238 1
< 0.1%
3.334 1
< 0.1%
3.7 2
< 0.1%
3.703 2
< 0.1%
3.888 1
< 0.1%
ValueCountFrequency (%)
9.172 1
< 0.1%
8.707 1
< 0.1%
8.702 1
< 0.1%
8.591 1
< 0.1%
8.573 1
< 0.1%
8.552 1
< 0.1%
8.54 1
< 0.1%
8.539 1
< 0.1%
8.515 1
< 0.1%
8.514 1
< 0.1%

vote_count
Real number (ℝ)

HIGH CORRELATION 

Distinct3298
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1936.5081
Minimum300
Maximum34495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:57:46.994392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile324.25
Q1466
median833
Q31922
95-th percentile7509
Maximum34495
Range34195
Interquartile range (IQR)1456

Descriptive statistics

Standard deviation3014.2472
Coefficient of variation (CV)1.5565374
Kurtosis20.66415
Mean1936.5081
Median Absolute Deviation (MAD)451
Skewness3.9515041
Sum17401462
Variance9085686.3
MonotonicityDecreasing
2024-05-13T11:57:48.061413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 28
 
0.3%
333 24
 
0.3%
323 24
 
0.3%
356 23
 
0.3%
313 22
 
0.2%
332 22
 
0.2%
335 22
 
0.2%
349 21
 
0.2%
398 20
 
0.2%
305 19
 
0.2%
Other values (3288) 8761
97.5%
ValueCountFrequency (%)
300 17
0.2%
301 18
0.2%
302 16
0.2%
303 18
0.2%
304 15
0.2%
305 19
0.2%
306 17
0.2%
307 17
0.2%
308 19
0.2%
309 17
0.2%
ValueCountFrequency (%)
34495 1
< 0.1%
32571 1
< 0.1%
30619 1
< 0.1%
29815 1
< 0.1%
29166 1
< 0.1%
28894 1
< 0.1%
27713 1
< 0.1%
27238 1
< 0.1%
26638 1
< 0.1%
25893 1
< 0.1%

status
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size640.6 KiB
Released
8986 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters71888
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 8986
100.0%

Length

2024-05-13T11:57:49.223874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-13T11:57:50.163463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
released 8986
100.0%

Most occurring characters

ValueCountFrequency (%)
e 26958
37.5%
R 8986
 
12.5%
l 8986
 
12.5%
a 8986
 
12.5%
s 8986
 
12.5%
d 8986
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 26958
37.5%
R 8986
 
12.5%
l 8986
 
12.5%
a 8986
 
12.5%
s 8986
 
12.5%
d 8986
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 26958
37.5%
R 8986
 
12.5%
l 8986
 
12.5%
a 8986
 
12.5%
s 8986
 
12.5%
d 8986
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 26958
37.5%
R 8986
 
12.5%
l 8986
 
12.5%
a 8986
 
12.5%
s 8986
 
12.5%
d 8986
 
12.5%
Distinct5687
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size140.4 KiB
Minimum1897-10-10 00:00:00
Maximum2023-09-22 00:00:00
2024-05-13T11:57:50.995342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:51.930467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6260
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70983058
Minimum0
Maximum2.923706 × 109
Zeros2458
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:57:53.443525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13415350
Q367586631
95-th percentile3.330083 × 108
Maximum2.923706 × 109
Range2.923706 × 109
Interquartile range (IQR)67586631

Descriptive statistics

Standard deviation1.628693 × 108
Coefficient of variation (CV)2.2944813
Kurtosis50.712901
Mean70983058
Median Absolute Deviation (MAD)13415350
Skewness5.6330524
Sum6.3785376 × 1011
Variance2.6526408 × 1016
MonotonicityNot monotonic
2024-05-13T11:57:54.149293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2458
 
27.4%
2000000 10
 
0.1%
10000000 10
 
0.1%
3000000 10
 
0.1%
1000000 10
 
0.1%
7000000 9
 
0.1%
12000000 9
 
0.1%
30000000 8
 
0.1%
11000000 8
 
0.1%
8000000 7
 
0.1%
Other values (6250) 6447
71.7%
ValueCountFrequency (%)
0 2458
27.4%
23 1
 
< 0.1%
43 1
 
< 0.1%
80 1
 
< 0.1%
84 1
 
< 0.1%
94 1
 
< 0.1%
201 1
 
< 0.1%
303 1
 
< 0.1%
1457 1
 
< 0.1%
1465 1
 
< 0.1%
ValueCountFrequency (%)
2923706026 1
< 0.1%
2800000000 1
< 0.1%
2320250281 1
< 0.1%
2264162353 1
< 0.1%
2068223624 1
< 0.1%
2052415039 1
< 0.1%
1921847111 1
< 0.1%
1671537444 1
< 0.1%
1663075401 1
< 0.1%
1518815515 1
< 0.1%

runtime
Real number (ℝ)

Distinct202
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.80625
Minimum0
Maximum366
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:57:54.963305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81
Q194
median104
Q3117
95-th percentile141
Maximum366
Range366
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.543101
Coefficient of variation (CV)0.21306019
Kurtosis8.0700781
Mean105.80625
Median Absolute Deviation (MAD)11
Skewness0.34942312
Sum950775
Variance508.1914
MonotonicityNot monotonic
2024-05-13T11:57:55.419581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 260
 
2.9%
95 250
 
2.8%
97 242
 
2.7%
105 241
 
2.7%
90 240
 
2.7%
98 238
 
2.6%
93 236
 
2.6%
94 221
 
2.5%
102 220
 
2.4%
96 219
 
2.4%
Other values (192) 6619
73.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 5
 
0.1%
4 9
0.1%
5 9
0.1%
6 11
0.1%
7 14
0.2%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
366 1
< 0.1%
317 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
242 2
< 0.1%
238 1
< 0.1%
236 1
< 0.1%
231 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%

adult
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.0 KiB
False
8986 
ValueCountFrequency (%)
False 8986
100.0%
2024-05-13T11:57:55.978709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct760
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23832993
Minimum0
Maximum4.6 × 108
Zeros2855
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:57:56.417631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8000000
Q330000000
95-th percentile1 × 108
Maximum4.6 × 108
Range4.6 × 108
Interquartile range (IQR)30000000

Descriptive statistics

Standard deviation39869059
Coefficient of variation (CV)1.6728516
Kurtosis12.281879
Mean23832993
Median Absolute Deviation (MAD)8000000
Skewness3.0258075
Sum2.1416327 × 1011
Variance1.5895419 × 1015
MonotonicityNot monotonic
2024-05-13T11:57:57.076027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2855
31.8%
20000000 246
 
2.7%
10000000 207
 
2.3%
30000000 206
 
2.3%
25000000 203
 
2.3%
15000000 197
 
2.2%
40000000 169
 
1.9%
5000000 147
 
1.6%
35000000 146
 
1.6%
50000000 135
 
1.5%
Other values (750) 4475
49.8%
ValueCountFrequency (%)
0 2855
31.8%
4 1
 
< 0.1%
7 1
 
< 0.1%
26 1
 
< 0.1%
35 2
 
< 0.1%
117 1
 
< 0.1%
119 1
 
< 0.1%
150 1
 
< 0.1%
275 1
 
< 0.1%
2000 1
 
< 0.1%
ValueCountFrequency (%)
460000000 1
 
< 0.1%
379000000 1
 
< 0.1%
365000000 1
 
< 0.1%
356000000 1
 
< 0.1%
340000000 1
 
< 0.1%
300000000 4
< 0.1%
294700000 1
 
< 0.1%
291000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 2
< 0.1%

original_language
Categorical

IMBALANCE 

Distinct42
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size588.0 KiB
en
7230 
fr
 
552
it
 
315
ja
 
243
es
 
160
Other values (37)
 
486

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters17972
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en 7230
80.5%
fr 552
 
6.1%
it 315
 
3.5%
ja 243
 
2.7%
es 160
 
1.8%
de 75
 
0.8%
ko 69
 
0.8%
cn 48
 
0.5%
zh 46
 
0.5%
ru 37
 
0.4%
Other values (32) 211
 
2.3%

Length

2024-05-13T11:57:57.669098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 7230
80.5%
fr 552
 
6.1%
it 315
 
3.5%
ja 243
 
2.7%
es 160
 
1.8%
de 75
 
0.8%
ko 69
 
0.8%
cn 48
 
0.5%
zh 46
 
0.5%
ru 37
 
0.4%
Other values (32) 211
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 7475
41.6%
n 7307
40.7%
r 603
 
3.4%
f 563
 
3.1%
t 363
 
2.0%
i 346
 
1.9%
a 279
 
1.6%
j 243
 
1.4%
s 201
 
1.1%
d 106
 
0.6%
Other values (13) 486
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 7475
41.6%
n 7307
40.7%
r 603
 
3.4%
f 563
 
3.1%
t 363
 
2.0%
i 346
 
1.9%
a 279
 
1.6%
j 243
 
1.4%
s 201
 
1.1%
d 106
 
0.6%
Other values (13) 486
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 7475
41.6%
n 7307
40.7%
r 603
 
3.4%
f 563
 
3.1%
t 363
 
2.0%
i 346
 
1.9%
a 279
 
1.6%
j 243
 
1.4%
s 201
 
1.1%
d 106
 
0.6%
Other values (13) 486
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 7475
41.6%
n 7307
40.7%
r 603
 
3.4%
f 563
 
3.1%
t 363
 
2.0%
i 346
 
1.9%
a 279
 
1.6%
j 243
 
1.4%
s 201
 
1.1%
d 106
 
0.6%
Other values (13) 486
 
2.7%
Distinct8983
Distinct (%)> 99.9%
Missing1
Missing (%)< 0.1%
Memory size3.7 MiB
2024-05-13T11:57:58.827861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length998
Median length588
Mean length270.32632
Min length14

Characters and Unicode

Total characters2428882
Distinct characters138
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8981 ?
Unique (%)> 99.9%

Sample

1st rowCobb, a skilled thief who commits corporate espionage by infiltrating the subconscious of his targets is offered a chance to regain his old life as payment for a task considered to be impossible: "inception", the implantation of another person's idea into a target's subconscious.
2nd rowThe adventures of a group of explorers who make use of a newly discovered wormhole to surpass the limitations on human space travel and conquer the vast distances involved in an interstellar voyage.
3rd rowBatman raises the stakes in his war on crime. With the help of Lt. Jim Gordon and District Attorney Harvey Dent, Batman sets out to dismantle the remaining criminal organizations that plague the streets. The partnership proves to be effective, but they soon find themselves prey to a reign of chaos unleashed by a rising criminal mastermind known to the terrified citizens of Gotham as the Joker.
4th rowIn the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.
5th rowWhen an unexpected enemy emerges and threatens global safety and security, Nick Fury, director of the international peacekeeping agency known as S.H.I.E.L.D., finds himself in need of a team to pull the world back from the brink of disaster. Spanning the globe, a daring recruitment effort begins!
ValueCountFrequency (%)
the 22286
 
5.4%
a 18551
 
4.5%
to 13845
 
3.3%
and 11835
 
2.9%
of 11325
 
2.7%
in 7514
 
1.8%
his 6729
 
1.6%
is 5394
 
1.3%
with 4104
 
1.0%
her 3777
 
0.9%
Other values (30799) 308913
74.6%
2024-05-13T11:58:02.690238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
405521
16.7%
e 236345
 
9.7%
t 162154
 
6.7%
a 158233
 
6.5%
i 142759
 
5.9%
n 140554
 
5.8%
o 139387
 
5.7%
s 130708
 
5.4%
r 129344
 
5.3%
h 102413
 
4.2%
Other values (128) 681464
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2428882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
405521
16.7%
e 236345
 
9.7%
t 162154
 
6.7%
a 158233
 
6.5%
i 142759
 
5.9%
n 140554
 
5.8%
o 139387
 
5.7%
s 130708
 
5.4%
r 129344
 
5.3%
h 102413
 
4.2%
Other values (128) 681464
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2428882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
405521
16.7%
e 236345
 
9.7%
t 162154
 
6.7%
a 158233
 
6.5%
i 142759
 
5.9%
n 140554
 
5.8%
o 139387
 
5.7%
s 130708
 
5.4%
r 129344
 
5.3%
h 102413
 
4.2%
Other values (128) 681464
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2428882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
405521
16.7%
e 236345
 
9.7%
t 162154
 
6.7%
a 158233
 
6.5%
i 142759
 
5.9%
n 140554
 
5.8%
o 139387
 
5.7%
s 130708
 
5.4%
r 129344
 
5.3%
h 102413
 
4.2%
Other values (128) 681464
28.1%

popularity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7686
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.549698
Minimum0.6
Maximum2994.357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.4 KiB
2024-05-13T11:58:03.663458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile8.1325
Q112.553
median16.6575
Q324.20475
95-th percentile54.072
Maximum2994.357
Range2993.757
Interquartile range (IQR)11.65175

Descriptive statistics

Standard deviation62.783311
Coefficient of variation (CV)2.5573965
Kurtosis1087.2261
Mean24.549698
Median Absolute Deviation (MAD)5.115
Skewness28.711234
Sum220603.59
Variance3941.7442
MonotonicityNot monotonic
2024-05-13T11:58:05.073562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.964 5
 
0.1%
12.54 5
 
0.1%
0.6 5
 
0.1%
12.968 5
 
0.1%
12.392 4
 
< 0.1%
13.667 4
 
< 0.1%
13.727 4
 
< 0.1%
12.147 4
 
< 0.1%
14.359 4
 
< 0.1%
14.658 4
 
< 0.1%
Other values (7676) 8942
99.5%
ValueCountFrequency (%)
0.6 5
0.1%
0.63 1
 
< 0.1%
0.681 1
 
< 0.1%
0.708 2
 
< 0.1%
0.719 1
 
< 0.1%
0.781 2
 
< 0.1%
0.986 1
 
< 0.1%
1.057 1
 
< 0.1%
1.071 1
 
< 0.1%
1.157 1
 
< 0.1%
ValueCountFrequency (%)
2994.357 1
< 0.1%
2680.593 1
< 0.1%
1692.778 1
< 0.1%
1567.273 1
< 0.1%
1458.514 1
< 0.1%
1175.267 1
< 0.1%
1111.036 1
< 0.1%
1069.34 1
< 0.1%
1008.942 1
< 0.1%
961.212 1
< 0.1%
Distinct8983
Distinct (%)100.0%
Missing3
Missing (%)< 0.1%
Memory size850.8 KiB
/oYuLEt3zVCKq57qu2F8dT7NIa6f.jpg
 
1
/aOxVYvQoAfNSns4hW5Ah55GBzgq.jpg
 
1
/Af6I9RZF0SIPeNnp8hAleGDTnKd.jpg
 
1
/dmO5x4STbulw9BlZaVRQF3tHg3t.jpg
 
1
/wZ0yyU6BnRANXbYbDjrYI6XZFsz.jpg
 
1
Other values (8978)
8978 

Length

Max length32
Median length32
Mean length31.971613
Min length30

Characters and Unicode

Total characters287201
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8983 ?
Unique (%)100.0%

Sample

1st row/oYuLEt3zVCKq57qu2F8dT7NIa6f.jpg
2nd row/gEU2QniE6E77NI6lCU6MxlNBvIx.jpg
3rd row/qJ2tW6WMUDux911r6m7haRef0WH.jpg
4th row/kyeqWdyUXW608qlYkRqosgbbJyK.jpg
5th row/RYMX2wcKCBAr24UyPD7xwmjaTn.jpg

Common Values

ValueCountFrequency (%)
/oYuLEt3zVCKq57qu2F8dT7NIa6f.jpg 1
 
< 0.1%
/aOxVYvQoAfNSns4hW5Ah55GBzgq.jpg 1
 
< 0.1%
/Af6I9RZF0SIPeNnp8hAleGDTnKd.jpg 1
 
< 0.1%
/dmO5x4STbulw9BlZaVRQF3tHg3t.jpg 1
 
< 0.1%
/wZ0yyU6BnRANXbYbDjrYI6XZFsz.jpg 1
 
< 0.1%
/npr7j2HuRgvsKrXLIxIiXevTH8A.jpg 1
 
< 0.1%
/5XKdP3FFaq81nmkDirMOWkIWJL7.jpg 1
 
< 0.1%
/gzc75Za4ArqfXIIr7STNnIE5rnA.jpg 1
 
< 0.1%
/qEr9fkGLQUWcAnVfDV46L79bdFs.jpg 1
 
< 0.1%
/pYfI62qgone3Ai0tzgxtQq7chDE.jpg 1
 
< 0.1%
Other values (8973) 8973
99.9%
(Missing) 3
 
< 0.1%

Length

2024-05-13T11:58:06.828439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oyulet3zvckq57qu2f8dt7nia6f.jpg 1
 
< 0.1%
d5iilfn5s0imszyzbpb8jpifbxd.jpg 1
 
< 0.1%
or06fn3dka5tukk1e9sl16pb3iy.jpg 1
 
< 0.1%
lyqbxzoqsue59ishyhrp0qiipaz.jpg 1
 
< 0.1%
7owy8vdww7thtzwh3okyrkwuld5.jpg 1
 
< 0.1%
78lptwv72etnqfw9cobyi0dwdja.jpg 1
 
< 0.1%
wumc08ipkeatf9rnmnxvidxqp4w.jpg 1
 
< 0.1%
arw2vcbvewovzr6pxd9xtd1tdqa.jpg 1
 
< 0.1%
r7vmzjiyzw9rpjmqjdxpjgicok9.jpg 1
 
< 0.1%
9xjzs2rlvxm8sfx8kpc3aigcoyq.jpg 1
 
< 0.1%
Other values (8973) 8973
99.9%

Most occurring characters

ValueCountFrequency (%)
j 13041
 
4.5%
p 13019
 
4.5%
g 12988
 
4.5%
/ 8983
 
3.1%
. 8983
 
3.1%
f 4096
 
1.4%
8 4094
 
1.4%
i 4087
 
1.4%
9 4074
 
1.4%
n 4071
 
1.4%
Other values (54) 209765
73.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 287201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
j 13041
 
4.5%
p 13019
 
4.5%
g 12988
 
4.5%
/ 8983
 
3.1%
. 8983
 
3.1%
f 4096
 
1.4%
8 4094
 
1.4%
i 4087
 
1.4%
9 4074
 
1.4%
n 4071
 
1.4%
Other values (54) 209765
73.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 287201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
j 13041
 
4.5%
p 13019
 
4.5%
g 12988
 
4.5%
/ 8983
 
3.1%
. 8983
 
3.1%
f 4096
 
1.4%
8 4094
 
1.4%
i 4087
 
1.4%
9 4074
 
1.4%
n 4071
 
1.4%
Other values (54) 209765
73.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 287201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
j 13041
 
4.5%
p 13019
 
4.5%
g 12988
 
4.5%
/ 8983
 
3.1%
. 8983
 
3.1%
f 4096
 
1.4%
8 4094
 
1.4%
i 4087
 
1.4%
9 4074
 
1.4%
n 4071
 
1.4%
Other values (54) 209765
73.0%
Common prefix/
Unique stems8983
Unique names8983
Unique extensions1
Unique directories1
Unique anchors1
ValueCountFrequency (%)
/oYuLEt3zVCKq57qu2F8dT7NIa6f.jpg 1
 
< 0.1%
/aOxVYvQoAfNSns4hW5Ah55GBzgq.jpg 1
 
< 0.1%
/Af6I9RZF0SIPeNnp8hAleGDTnKd.jpg 1
 
< 0.1%
/dmO5x4STbulw9BlZaVRQF3tHg3t.jpg 1
 
< 0.1%
/wZ0yyU6BnRANXbYbDjrYI6XZFsz.jpg 1
 
< 0.1%
/npr7j2HuRgvsKrXLIxIiXevTH8A.jpg 1
 
< 0.1%
/5XKdP3FFaq81nmkDirMOWkIWJL7.jpg 1
 
< 0.1%
/gzc75Za4ArqfXIIr7STNnIE5rnA.jpg 1
 
< 0.1%
/qEr9fkGLQUWcAnVfDV46L79bdFs.jpg 1
 
< 0.1%
/pYfI62qgone3Ai0tzgxtQq7chDE.jpg 1
 
< 0.1%
Other values (8973) 8973
99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
/oYuLEt3zVCKq57qu2F8dT7NIa6f 1
 
< 0.1%
/pYfI62qgone3Ai0tzgxtQq7chDE 1
 
< 0.1%
/dmO5x4STbulw9BlZaVRQF3tHg3t 1
 
< 0.1%
/wZ0yyU6BnRANXbYbDjrYI6XZFsz 1
 
< 0.1%
/npr7j2HuRgvsKrXLIxIiXevTH8A 1
 
< 0.1%
/5XKdP3FFaq81nmkDirMOWkIWJL7 1
 
< 0.1%
/gzc75Za4ArqfXIIr7STNnIE5rnA 1
 
< 0.1%
/qEr9fkGLQUWcAnVfDV46L79bdFs 1
 
< 0.1%
/75ot83QOkc02vujyzmIbumQCU6Y 1
 
< 0.1%
/z1oNjotUI7D06J4LWQFQzdIuPnf 1
 
< 0.1%
Other values (8973) 8973
99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
oYuLEt3zVCKq57qu2F8dT7NIa6f.jpg 1
 
< 0.1%
pYfI62qgone3Ai0tzgxtQq7chDE.jpg 1
 
< 0.1%
dmO5x4STbulw9BlZaVRQF3tHg3t.jpg 1
 
< 0.1%
wZ0yyU6BnRANXbYbDjrYI6XZFsz.jpg 1
 
< 0.1%
npr7j2HuRgvsKrXLIxIiXevTH8A.jpg 1
 
< 0.1%
5XKdP3FFaq81nmkDirMOWkIWJL7.jpg 1
 
< 0.1%
gzc75Za4ArqfXIIr7STNnIE5rnA.jpg 1
 
< 0.1%
qEr9fkGLQUWcAnVfDV46L79bdFs.jpg 1
 
< 0.1%
75ot83QOkc02vujyzmIbumQCU6Y.jpg 1
 
< 0.1%
z1oNjotUI7D06J4LWQFQzdIuPnf.jpg 1
 
< 0.1%
Other values (8973) 8973
99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
.jpg 8983
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
/ 8983
> 99.9%
(Missing) 3
 
< 0.1%
ValueCountFrequency (%)
8983
> 99.9%
(Missing) 3
 
< 0.1%

genres
Text

Distinct1988
Distinct (%)22.1%
Missing2
Missing (%)< 0.1%
Memory size754.4 KiB
2024-05-13T11:58:08.591390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length86
Median length59
Mean length20.975623
Min length3

Characters and Unicode

Total characters188445
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1223 ?
Unique (%)13.6%

Sample

1st rowAction, Science Fiction, Adventure
2nd rowAdventure, Drama, Science Fiction
3rd rowDrama, Action, Crime, Thriller
4th rowAction, Adventure, Fantasy, Science Fiction
5th rowScience Fiction, Action, Adventure
ValueCountFrequency (%)
drama 3861
15.8%
comedy 3229
13.2%
thriller 2432
9.9%
action 2100
8.6%
adventure 1510
 
6.2%
romance 1466
 
6.0%
crime 1317
 
5.4%
horror 1283
 
5.2%
science 1091
 
4.5%
fiction 1091
 
4.5%
Other values (11) 5110
20.9%
2024-05-13T11:58:10.390420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 17185
 
9.1%
15506
 
8.2%
e 14967
 
7.9%
, 14315
 
7.6%
a 13461
 
7.1%
i 12563
 
6.7%
o 11898
 
6.3%
m 11838
 
6.3%
n 10151
 
5.4%
t 8031
 
4.3%
Other values (19) 58530
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 17185
 
9.1%
15506
 
8.2%
e 14967
 
7.9%
, 14315
 
7.6%
a 13461
 
7.1%
i 12563
 
6.7%
o 11898
 
6.3%
m 11838
 
6.3%
n 10151
 
5.4%
t 8031
 
4.3%
Other values (19) 58530
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 17185
 
9.1%
15506
 
8.2%
e 14967
 
7.9%
, 14315
 
7.6%
a 13461
 
7.1%
i 12563
 
6.7%
o 11898
 
6.3%
m 11838
 
6.3%
n 10151
 
5.4%
t 8031
 
4.3%
Other values (19) 58530
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 17185
 
9.1%
15506
 
8.2%
e 14967
 
7.9%
, 14315
 
7.6%
a 13461
 
7.1%
i 12563
 
6.7%
o 11898
 
6.3%
m 11838
 
6.3%
n 10151
 
5.4%
t 8031
 
4.3%
Other values (19) 58530
31.1%
Distinct7721
Distinct (%)86.3%
Missing44
Missing (%)0.5%
Memory size1.2 MiB
2024-05-13T11:58:10.999212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length549
Median length251
Mean length64.218967
Min length2

Characters and Unicode

Total characters574246
Distinct characters116
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7338 ?
Unique (%)82.1%

Sample

1st rowLegendary Pictures, Syncopy, Warner Bros. Pictures
2nd rowLegendary Pictures, Syncopy, Lynda Obst Productions
3rd rowDC Comics, Legendary Pictures, Syncopy, Isobel Griffiths, Warner Bros. Pictures
4th rowDune Entertainment, Lightstorm Entertainment, 20th Century Fox, Ingenious Media
5th rowMarvel Studios
ValueCountFrequency (%)
pictures 4876
 
6.7%
films 3922
 
5.4%
productions 3652
 
5.1%
entertainment 3137
 
4.3%
film 1709
 
2.4%
media 861
 
1.2%
warner 740
 
1.0%
bros 712
 
1.0%
the 666
 
0.9%
company 666
 
0.9%
Other values (8503) 51329
71.0%
2024-05-13T11:58:11.934730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63331
 
11.0%
i 42478
 
7.4%
e 40824
 
7.1%
n 38651
 
6.7%
t 36543
 
6.4%
r 33786
 
5.9%
a 31470
 
5.5%
o 30926
 
5.4%
s 26033
 
4.5%
, 22612
 
3.9%
Other values (106) 207592
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 574246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
63331
 
11.0%
i 42478
 
7.4%
e 40824
 
7.1%
n 38651
 
6.7%
t 36543
 
6.4%
r 33786
 
5.9%
a 31470
 
5.5%
o 30926
 
5.4%
s 26033
 
4.5%
, 22612
 
3.9%
Other values (106) 207592
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 574246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
63331
 
11.0%
i 42478
 
7.4%
e 40824
 
7.1%
n 38651
 
6.7%
t 36543
 
6.4%
r 33786
 
5.9%
a 31470
 
5.5%
o 30926
 
5.4%
s 26033
 
4.5%
, 22612
 
3.9%
Other values (106) 207592
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 574246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
63331
 
11.0%
i 42478
 
7.4%
e 40824
 
7.1%
n 38651
 
6.7%
t 36543
 
6.4%
r 33786
 
5.9%
a 31470
 
5.5%
o 30926
 
5.4%
s 26033
 
4.5%
, 22612
 
3.9%
Other values (106) 207592
36.2%
Distinct870
Distinct (%)9.7%
Missing9
Missing (%)0.1%
Memory size787.3 KiB
2024-05-13T11:58:12.376132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length173
Median length24
Mean length24.766403
Min length4

Characters and Unicode

Total characters222328
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique624 ?
Unique (%)7.0%

Sample

1st rowUnited Kingdom, United States of America
2nd rowUnited Kingdom, United States of America
3rd rowUnited Kingdom, United States of America
4th rowUnited States of America, United Kingdom
5th rowUnited States of America
ValueCountFrequency (%)
united 7975
22.8%
america 6664
19.0%
states 6664
19.0%
of 6664
19.0%
kingdom 1289
 
3.7%
france 1135
 
3.2%
germany 547
 
1.6%
canada 538
 
1.5%
italy 472
 
1.3%
japan 368
 
1.1%
Other values (109) 2725
 
7.8%
2024-05-13T11:58:13.180713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26064
11.7%
e 24303
 
10.9%
t 22333
 
10.0%
a 19867
 
8.9%
i 17402
 
7.8%
n 13371
 
6.0%
d 10325
 
4.6%
r 9335
 
4.2%
m 8870
 
4.0%
o 8842
 
4.0%
Other values (40) 61616
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 222328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26064
11.7%
e 24303
 
10.9%
t 22333
 
10.0%
a 19867
 
8.9%
i 17402
 
7.8%
n 13371
 
6.0%
d 10325
 
4.6%
r 9335
 
4.2%
m 8870
 
4.0%
o 8842
 
4.0%
Other values (40) 61616
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 222328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26064
11.7%
e 24303
 
10.9%
t 22333
 
10.0%
a 19867
 
8.9%
i 17402
 
7.8%
n 13371
 
6.0%
d 10325
 
4.6%
r 9335
 
4.2%
m 8870
 
4.0%
o 8842
 
4.0%
Other values (40) 61616
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 222328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26064
11.7%
e 24303
 
10.9%
t 22333
 
10.0%
a 19867
 
8.9%
i 17402
 
7.8%
n 13371
 
6.0%
d 10325
 
4.6%
r 9335
 
4.2%
m 8870
 
4.0%
o 8842
 
4.0%
Other values (40) 61616
27.7%

keywords
Text

MISSING 

Distinct8558
Distinct (%)98.8%
Missing328
Missing (%)3.7%
Memory size1.7 MiB
2024-05-13T11:58:13.754713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1058
Median length369
Mean length130.99469
Min length3

Characters and Unicode

Total characters1134152
Distinct characters147
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8513 ?
Unique (%)98.3%

Sample

1st rowrescue, mission, dream, airplane, paris, france, virtual reality, kidnapping, philosophy, spy, allegory, manipulation, car crash, heist, memory, architecture, los angeles, california, dream world, subconscious
2nd rowrescue, future, spacecraft, race against time, artificial intelligence (a.i.), nasa, time warp, dystopia, expedition, space travel, wormhole, famine, black hole, quantum mechanics, family relationships, space, robot, astronaut, scientist, single father, farmer, space station, curious, space adventure, time paradox, thoughtful, time-manipulation, father daughter relationship, 2060s, cornfield, time manipulation, complicated
3rd rowjoker, sadism, chaos, secret identity, crime fighter, superhero, anti hero, scarecrow, based on comic, vigilante, organized crime, tragic hero, anti villain, criminal mastermind, district attorney, super power, super villain, neo-noir
4th rowfuture, society, culture clash, space travel, space war, space colony, tribe, romance, alien, futuristic, space, alien planet, marine, soldier, battle, love affair, nature, anti war, power relations, joyful
5th rownew york city, superhero, shield, based on comic, alien invasion, superhero team, aftercreditsstinger, duringcreditsstinger, marvel cinematic universe (mcu)
ValueCountFrequency (%)
on 2579
 
1.8%
based 2429
 
1.7%
relationship 1625
 
1.1%
of 1333
 
0.9%
or 1219
 
0.8%
novel 1161
 
0.8%
book 1136
 
0.8%
murder 848
 
0.6%
love 829
 
0.6%
new 804
 
0.6%
Other values (10250) 130474
90.3%
2024-05-13T11:58:14.647057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135795
12.0%
e 94539
 
8.3%
, 82257
 
7.3%
a 79043
 
7.0%
i 78258
 
6.9%
r 74159
 
6.5%
o 69424
 
6.1%
n 65196
 
5.7%
t 60443
 
5.3%
s 58590
 
5.2%
Other values (137) 336448
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1134152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
135795
12.0%
e 94539
 
8.3%
, 82257
 
7.3%
a 79043
 
7.0%
i 78258
 
6.9%
r 74159
 
6.5%
o 69424
 
6.1%
n 65196
 
5.7%
t 60443
 
5.3%
s 58590
 
5.2%
Other values (137) 336448
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1134152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
135795
12.0%
e 94539
 
8.3%
, 82257
 
7.3%
a 79043
 
7.0%
i 78258
 
6.9%
r 74159
 
6.5%
o 69424
 
6.1%
n 65196
 
5.7%
t 60443
 
5.3%
s 58590
 
5.2%
Other values (137) 336448
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1134152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
135795
12.0%
e 94539
 
8.3%
, 82257
 
7.3%
a 79043
 
7.0%
i 78258
 
6.9%
r 74159
 
6.5%
o 69424
 
6.1%
n 65196
 
5.7%
t 60443
 
5.3%
s 58590
 
5.2%
Other values (137) 336448
29.7%

Interactions

2024-05-13T11:57:35.585122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:22.128344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:25.866058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:27.827236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:29.738560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:31.786613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:33.625153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:35.857131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:22.632661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:26.184504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:28.105101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:30.026406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:32.048877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:33.871439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:36.156593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:23.089110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:26.442175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:28.374381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:30.305981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:32.323400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:34.167811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:36.534812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:23.517015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:26.704482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:28.642212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:30.571253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:32.587325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:34.464433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:36.948006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:23.979250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:26.988090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:28.925012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:30.837016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:32.843339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:34.765179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:37.368594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:24.462191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:27.289080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:29.208764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:31.098819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:33.126249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:35.036217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:37.766344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:25.211124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:27.550951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:29.470566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:31.531273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:33.368140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-13T11:57:35.318665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-13T11:58:14.927404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
budgetidoriginal_languagepopularityrevenueruntimevote_averagevote_count
budget1.000-0.2560.0000.4420.7610.307-0.1110.551
id-0.2561.0000.075-0.119-0.308-0.086-0.067-0.152
original_language0.0000.0751.000-0.200-0.2570.0210.170-0.218
popularity0.442-0.119-0.2001.0000.5100.1730.1600.612
revenue0.761-0.308-0.2570.5101.0000.2960.0110.632
runtime0.307-0.0860.0210.1730.2961.0000.2970.237
vote_average-0.111-0.0670.1700.1600.0110.2971.0000.204
vote_count0.551-0.152-0.2180.6120.6320.2370.2041.000

Missing values

2024-05-13T11:57:38.423281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-13T11:57:39.695405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-13T11:57:41.004741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idtitlevote_averagevote_countstatusrelease_daterevenueruntimeadultbudgetoriginal_languageoverviewpopularityposter_pathgenresproduction_companiesproduction_countrieskeywords
027205Inception8.36434495Released2010-07-15825532764148False160000000enCobb, a skilled thief who commits corporate espionage by infiltrating the subconscious of his targets is offered a chance to regain his old life as payment for a task considered to be impossible: "inception", the implantation of another person's idea into a target's subconscious.83.952/oYuLEt3zVCKq57qu2F8dT7NIa6f.jpgAction, Science Fiction, AdventureLegendary Pictures, Syncopy, Warner Bros. PicturesUnited Kingdom, United States of Americarescue, mission, dream, airplane, paris, france, virtual reality, kidnapping, philosophy, spy, allegory, manipulation, car crash, heist, memory, architecture, los angeles, california, dream world, subconscious
1157336Interstellar8.41732571Released2014-11-05701729206169False165000000enThe adventures of a group of explorers who make use of a newly discovered wormhole to surpass the limitations on human space travel and conquer the vast distances involved in an interstellar voyage.140.241/gEU2QniE6E77NI6lCU6MxlNBvIx.jpgAdventure, Drama, Science FictionLegendary Pictures, Syncopy, Lynda Obst ProductionsUnited Kingdom, United States of Americarescue, future, spacecraft, race against time, artificial intelligence (a.i.), nasa, time warp, dystopia, expedition, space travel, wormhole, famine, black hole, quantum mechanics, family relationships, space, robot, astronaut, scientist, single father, farmer, space station, curious, space adventure, time paradox, thoughtful, time-manipulation, father daughter relationship, 2060s, cornfield, time manipulation, complicated
2155The Dark Knight8.51230619Released2008-07-161004558444152False185000000enBatman raises the stakes in his war on crime. With the help of Lt. Jim Gordon and District Attorney Harvey Dent, Batman sets out to dismantle the remaining criminal organizations that plague the streets. The partnership proves to be effective, but they soon find themselves prey to a reign of chaos unleashed by a rising criminal mastermind known to the terrified citizens of Gotham as the Joker.130.643/qJ2tW6WMUDux911r6m7haRef0WH.jpgDrama, Action, Crime, ThrillerDC Comics, Legendary Pictures, Syncopy, Isobel Griffiths, Warner Bros. PicturesUnited Kingdom, United States of Americajoker, sadism, chaos, secret identity, crime fighter, superhero, anti hero, scarecrow, based on comic, vigilante, organized crime, tragic hero, anti villain, criminal mastermind, district attorney, super power, super villain, neo-noir
319995Avatar7.57329815Released2009-12-152923706026162False237000000enIn the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following orders and protecting an alien civilization.79.932/kyeqWdyUXW608qlYkRqosgbbJyK.jpgAction, Adventure, Fantasy, Science FictionDune Entertainment, Lightstorm Entertainment, 20th Century Fox, Ingenious MediaUnited States of America, United Kingdomfuture, society, culture clash, space travel, space war, space colony, tribe, romance, alien, futuristic, space, alien planet, marine, soldier, battle, love affair, nature, anti war, power relations, joyful
424428The Avengers7.71029166Released2012-04-251518815515143False220000000enWhen an unexpected enemy emerges and threatens global safety and security, Nick Fury, director of the international peacekeeping agency known as S.H.I.E.L.D., finds himself in need of a team to pull the world back from the brink of disaster. Spanning the globe, a daring recruitment effort begins!98.082/RYMX2wcKCBAr24UyPD7xwmjaTn.jpgScience Fiction, Action, AdventureMarvel StudiosUnited States of Americanew york city, superhero, shield, based on comic, alien invasion, superhero team, aftercreditsstinger, duringcreditsstinger, marvel cinematic universe (mcu)
5293660Deadpool7.60628894Released2016-02-09783100000108False58000000enThe origin story of former Special Forces operative turned mercenary Wade Wilson, who, after being subjected to a rogue experiment that leaves him with accelerated healing powers, adopts the alter ego Deadpool. Armed with his new abilities and a dark, twisted sense of humor, Deadpool hunts down the man who nearly destroyed his life.72.735/zq8Cl3PNIDGU3iWNRoc5nEZ6pCe.jpgAction, Adventure, Comedy20th Century Fox, The Donners' Company, Genre FilmsUnited States of Americasuperhero, anti hero, mercenary, based on comic, aftercreditsstinger, duringcreditsstinger
6299536Avengers: Infinity War8.25527713Released2018-04-252052415039149False300000000enAs the Avengers and their allies have continued to protect the world from threats too large for any one hero to handle, a new danger has emerged from the cosmic shadows: Thanos. A despot of intergalactic infamy, his goal is to collect all six Infinity Stones, artifacts of unimaginable power, and use them to inflict his twisted will on all of reality. Everything the Avengers have fought for has led up to this moment - the fate of Earth and existence itself has never been more uncertain.154.340/7WsyChQLEftFiDOVTGkv3hFpyyt.jpgAdventure, Action, Science FictionMarvel StudiosUnited States of Americasacrifice, magic, superhero, based on comic, space, battlefield, genocide, magical object, super power, aftercreditsstinger, marvel cinematic universe (mcu), cosmic
7550Fight Club8.43827238Released1999-10-15100853753139False63000000enA ticking-time-bomb insomniac and a slippery soap salesman channel primal male aggression into a shocking new form of therapy. Their concept catches on, with underground "fight clubs" forming in every town, until an eccentric gets in the way and ignites an out-of-control spiral toward oblivion.69.498/pB8BM7pdSp6B6Ih7QZ4DrQ3PmJK.jpgDramaRegency Enterprises, Fox 2000 Pictures, Taurus Film, Atman Entertainment, Knickerbocker Films, The Linson Company, 20th Century FoxUnited States of Americadual identity, rage and hate, based on novel or book, nihilism, fight, support group, dystopia, insomnia, alter ego, breaking the fourth wall, split personality, quitting a job, dissociative identity disorder, self destructiveness
8118340Guardians of the Galaxy7.90626638Released2014-07-30772776600121False170000000enLight years from Earth, 26 years after being abducted, Peter Quill finds himself the prime target of a manhunt after discovering an orb wanted by Ronan the Accuser.33.255/r7vmZjiyZw9rpJMQJdXpjgiCOk9.jpgAction, Science Fiction, AdventureMarvel StudiosUnited States of Americaspacecraft, based on comic, space, orphan, adventurer, aftercreditsstinger, duringcreditsstinger, marvel cinematic universe (mcu)
9680Pulp Fiction8.48825893Released1994-09-10213900000154False8500000enA burger-loving hit man, his philosophical partner, a drug-addled gangster's moll and a washed-up boxer converge in this sprawling, comedic crime caper. Their adventures unfurl in three stories that ingeniously trip back and forth in time.74.862/d5iIlFn5s0ImszYzBPb8JPIfbXD.jpgThriller, CrimeMiramax, A Band Apart, Jersey FilmsUnited States of Americadrug dealer, boxer, massage, stolen money, briefcase, crime boss, redemption, heirloom, dance competition, los angeles, california, theft, nonlinear timeline, multiple storylines, neo-noir, hilarious
idtitlevote_averagevote_countstatusrelease_daterevenueruntimeadultbudgetoriginal_languageoverviewpopularityposter_pathgenresproduction_companiesproduction_countrieskeywords
8977391710A Futile and Stupid Gesture6.463300Released2018-01-240101False10000000enIn a life full of triumph and failure, "National Lampoon" co-founder Doug Kenney built a comedy empire, molding pop culture in the 1970s.12.869/kyHBeRgbbhs7kVrZYblVj9u2Xbd.jpgComedyAbominable Pictures, Principato-Young EntertainmentUnited States of Americabiography
897811564Class of 19846.446300Released1982-08-20098False4300000enAndy is a new teacher at an inner city high school that is unlike any he has seen before. There are metal detectors at the front door and the place is basically run by a tough kid named Peter Stegman. Soon, Andy and Stegman become enemies and Stegman will stop at nothing to protect his turf and drug dealing business.13.865/zaPeoSMiWM23g2M8qVMfNJdEeQa.jpgAction, Crime, Drama, ThrillerGuerilla High Productions, PSO, United Film Distribution Company (UFDC)Canadahigh school, detective, music teacher, violence in schools, punk rock, teacher, murder, vigilante, gang, juvenile delinquent, canuxploitation
897910900Surveillance5.955300Released2008-02-08113832297False3500000enAn FBI agent tracks a serial killer with the help of three of his would-be victims - all of whom have wildly different stories to tell.8.881/8CYX53rLeRonQjJOKyHkwAwBIWD.jpgHorror, Thriller, MysterySee Film, Blue Rider Pictures, Lago FilmsUnited States of Americadying and death, police, sadism, mass murder, fbi, camera, investigation, lovers, murder, series of murders, drugs, witness to murder, surveillance, woman director, murder hunt
898011962Joe's Apartment5.500300Released1996-07-26461901478False13000000enA nice guy has just moved to New York and discovers that he must share his run-down apartment with a couple thousand singing, dancing cockroaches.19.853/7OBbURUtU37ucxHGUVkD598f6t3.jpgComedy, FantasyGeffen Pictures, MTV Films, Warner Bros. PicturesUnited States of Americaflat, musical, lodger, cockroach, rent, spekulant
898140016Birdemic: Shock and Terror2.098300Released2010-02-27093False10000enA platoon of eagles and vultures attacks the residents of a small town. Many people die. It's not known what caused the flying menace to attack. Two people manage to fight back, but will they survive Birdemic?9.521/gqmcAVNNUosB55RliecFYnkWT4M.jpgThriller, Fantasy, Romance, Horror, Science FictionMoviehead PicturesUnited States of Americabird attack, nature run amok, psychotronic, romantic, matter of fact
89822728Postal4.572300Released2007-10-18146741102False15000000enThe story begins with a regular Joe who tries desperately to seek employment, but embarks on a violent rampage when he teams up with cult leader Uncle Dave. Their first act is to heist an amusement park, only to learn that the Taliban are planning the same heist as well. Chaos ensues, and now the Postal Dude must not only take on terrorists but also political figures.8.932/2U91KS7t6HA1dI6YMiA6TpPRVqU.jpgAction, ComedyVivendi Entertainment, Running With ScissorsCanadagun rampage, machinegun, based on video game, cruel, euphoric
898310937Barfly6.900300Released1987-09-023221568100False3000000enDowntrodden writer Henry and distressed goddess Wanda aren't exactly husband and wife: they're wedded to their bar stools. But, they like each other's company—and Barfly captures their giddy, gin-soaked attempts to make a go of life on the skids.11.604/1TDhyo38sHKU9WSwdVV0AtTrmTS.jpgComedy, Drama, RomanceGolan-Globus Productions, Cannon Group, American ZoetropeUnited States of Americacareer, prostitute, fight, bartender, poet, alcoholism, money, los angeles, california, alcohol abuse, paramedic, boyfriend girlfriend relationship, street fight, barflies, unemployed, brawler, brawlers, barfly
8984243442Un fantastico via vai5.990300Released2013-12-12095False0itMiddle-aged family man Arnaldo is kicked out by his wife because of a misunderstanding. Instead of despairing, Arnaldo takes advantage of the situation to turn around his unsatisfying adult life by going to live in a flat shared with four university students.6.625/xUAabAY6cPvFDbtZ5tD8WJjvpQX.jpgComedyLevante, RAI CinemaItalyNaN
8985591278Game of Thrones: The Last Watch7.100300Released2019-05-260115False0enFor a year, acclaimed British filmmaker Jeanie Finlay was embedded on the set of the hit HBO series “Game of Thrones,” chronicling the creation of the show’s most ambitious and complicated season. Debuting one week after the series 8 finale, GAME OF THRONES: THE LAST WATCH delves deep into the mud and blood to reveal the tears and triumphs involved in the challenge of bringing the fantasy world of Westeros to life in the very real studios, fields and car-parks of Northern Ireland. Made with unprecedented access, GAME OF THRONES: THE LAST WATCH is an up-close and personal portrait from the trenches of production, following the crew and the cast as they contend with extreme weather, punishing deadlines and an ever-excited fandom hungry for spoilers. Much more than a “making of” documentary, this is a funny, heartbreaking story, told with wit and intimacy, about the bittersweet pleasures of what it means to create a world – and then have to say goodbye to it.11.614/tVyKpOYDtKzs4SOYCbZn4YNFWHI.jpgDocumentary, TV MovieGlimmer Films, HBOUnited States of Americamaking of
8986204553Cold Eyes7.475300Released2013-07-0337795598119False0koHa Yoon-ju becomes the newest member of a unit within the Korean Police Forces Special Crime Department that specializes in surveillance activities on high-profile criminals. She teams up with Hwang Sang-jun, the veteran leader of the unit, and tries to track down James who is the cold-hearted leader of an armed criminal organization.57.107/77Srq9bAJkB1RE1GBAyBA9Y6ivN.jpgAdventure, Crime, Action, ThrillerNext Entertainment World, Zip Cinema, Opus Pictures, Union Investment Partners, Sundream Motion Pictures, Seoul Film Commission, United Pictures, CJ EntertainmentHong Kong, South Koreacold eyes